Bayesian Neural Networks (BNNs) are stochastic neural networks trained using a
Bayesian approach. While standard neural network training performs a maximum likelihood estimation (MLE) of the parameters of the network \(\mathbf {\omega }\! \in \!\Omega \) , training BNNs extends to estimating the posterior distribution.
While the formulation is straightforward (see App. ), due to the integration with respect to the whole parameter space \(\Omega \)  this computation is intractable for DNNs.
In this work, we train the BNNs with two common training methods:  Mean-Field Variational Inference (MFVI) [1]}, and Hamiltonian Monte Carlo (HMC) [2]}—the latter expected to be more accurate.
